What is Gini impurity?
Understand the Problem
The question asks for a definition of Gini impurity. Gini impurity is a measure of how often a randomly chosen element from the set would be incorrectly labeled if it was randomly labeled according to the distribution of labels in the subset.
Answer
Gini impurity measures how well a dataset should be split in a decision tree.
Gini impurity is a measure used in decision trees to determine how well a dataset should be split. It calculates the probability of incorrectly classifying a randomly chosen element if it were randomly labeled.
Answer for screen readers
Gini impurity is a measure used in decision trees to determine how well a dataset should be split. It calculates the probability of incorrectly classifying a randomly chosen element if it were randomly labeled.
More Information
The Gini impurity is used together with decision trees. Decision trees are a type of supervised machine learning algorithm used for both classification and regression tasks.
Tips
A common mistake is confusing Gini Impurity with Entropy, another measure of impurity. Gini impurity is computationally faster, while entropy might provide slightly more balanced trees.
Sources
- Gini Impurity - LearnDataSci - learndatasci.com
- A Simple Explanation of Gini Impurity - victorzhou.com - victorzhou.com
- ML | Gini Impurity and Entropy in Decision Tree - GeeksforGeeks - geeksforgeeks.org
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